agent-blueprint

name	agent-blueprint
description	Build production-grade AI agents from scratch using patterns inspired by production-grade agentic systems like Claude Code. Covers two audiences — (1) workflow builders who need planner+executor pipelines, human-in-the-loop checkpoints, and multi-agent orchestration, and (2) application embedders who need HTTP/SSE APIs, multi-user session management, and React frontend integration. Use when asked to build an AI agent, create a coding assistant, embed an agent in an app, build an API agent, clone Claude Code, build a CLI agent, design an agent architecture, scaffold an agentic system, or automate a multi-step workflow. Triggers on keywords like agent builder, agent blueprint, coding assistant, CLI agent, agentic tool, claude code clone, agent scaffold, agent architecture, workflow automation, multi-agent, planner executor, SSE streaming, agent API.
license	Apache-2.0
metadata	{"version":"1.0.0","author":"agent-blueprint-community"}

Agent Blueprint - Build Production-Grade AI Agents

Generate complete, working AI agents using battle-tested patterns studied from production-grade agentic systems. Every pattern is independently implemented and adapted for any framework, language, or purpose.

When to Use

Use this skill when:

• Building any kind of AI agent or agentic tool
• Creating a coding assistant, research agent, or task automation agent
• Designing an agent architecture with tools, permissions, and context management
• Scaffolding a new agent project from scratch
• Cloning or recreating Claude Code-like functionality
• Building multi-step automated workflows with planner + executor architecture
• Embedding an agent inside a web application via HTTP/SSE API
• Adding human-in-the-loop approval checkpoints to an autonomous agent
• Making an agent production-ready (observability, cost control, long tasks)

Do NOT use when:

• Building a simple chatbot without tools (use prompt engineering instead)
• Creating a non-agentic API wrapper (use your SDK directly)

The 5-Phase Build Process

Phase 1: Define Your Agent

Before writing any code, answer these 4 questions. Write answers to a AGENT.md spec file in your project:

1. PURPOSE: What does this agent do in one sentence?
2. TOOLS: What actions can it take? (file ops, shell, web, APIs, custom)
3. TRIGGERS: When should it act autonomously vs wait for input?
4. OUTPUT: What does "done" look like? (code changes, reports, files, etc.)

Based on your answers, choose a template:

Template	Best For	Complexity
minimal-agent	Single-purpose agents, scripts, tools	Low (~200 lines, Python)
coding-agent	Full coding assistants with file ops	Medium (~800 lines, Python)
research-agent	Research, analysis, web search	Medium (~600 lines, Python)
task-agent	Multi-step automation, orchestration	High (~1200 lines, Python)

For TypeScript/Bun: use references/typescript-agent-loop.md as your starting point and examples/typescript-research-agent.md as a runnable example. For Rust: use references/rust-agent.md.

Not sure which template? Use this quick selector:

Your goal	Template	Key references to load
CLI tool that does one thing	minimal-agent	agent-loop.md
Coding assistant in terminal	coding-agent	tool-system.md, permission-system.md
Automated multi-step workflow	workflow-agent	planner-executor.md, human-in-the-loop.md
Agent inside a web/mobile app	api-agent	serving.md, long-tasks.md
Research + report generation	research-agent	context-management.md, memory.md
100+ step autonomous task	task-agent	long-tasks.md, production-principles.md

Phase 2: Implement Core Components

Build these 5 components in order. Each is a separate module:

agent/
  core/
    agent_loop.py      # The main while-true loop (Phase 2.1)
    tool_registry.py   # Tool definitions and dispatch (Phase 2.2)
    permissions.py     # Allow/deny/ask permission system (Phase 2.3)
    context.py         # Message history and compaction (Phase 2.4)
    system_prompt.py   # Dynamic prompt assembly (Phase 2.5)

Phase 2.1: The Agent Loop

The heart of every agent. This is the while(true) pattern that drives all agentic behavior:

async def agent_loop(messages, tools, system_prompt, api_client):
    while True:
        # 1. Manage context window
        messages = compact_if_needed(messages)

        # 2. Call the LLM with streaming
        response = await api_client.stream(
            system=system_prompt,
            messages=messages,
            tools=tools.to_api_schema(),
        )

        # 3. Process response blocks
        assistant_content = []
        for block in response:
            if block.type == "text":
                assistant_content.append(block)
            elif block.type == "tool_use":
                # 4. Check permissions
                decision = permissions.check(block.name, block.input)
                if decision == "deny":
                    assistant_content.append(tool_error(block.id, "Permission denied"))
                    continue

                # 5. Execute tool
                result = await tools.execute(block.name, block.input)
                messages.append(tool_result(block.id, result))
                assistant_content.append(block)

        messages.append(assistant_message(assistant_content))

        # 6. Continue if tools were used, stop if text-only
        if not any(b.type == "tool_use" for b in assistant_content):
            break

    return messages

Critical patterns:

• Stream responses for real-time feedback
• Execute concurrency-safe tools in parallel
• Handle prompt_too_long errors by compacting context
• Track token usage and cost per turn

Load references/agent-loop.md for the complete loop with error recovery, streaming, and cost tracking.

Phase 2.2: The Tool System

Every tool implements this interface:

class Tool:
    name: str                    # Unique identifier (e.g., "bash", "read_file")
    description: str             # What it does (shown to LLM)
    input_schema: dict           # JSON Schema for parameters
    is_read_only: bool           # True if no side effects
    is_concurrency_safe: bool    # True if parallel execution is safe

    async def call(self, input: dict, context: dict) -> ToolResult:
        """Execute the tool and return result."""

    def check_permissions(self, input: dict) -> PermissionDecision:
        """Return allow/deny/ask for this specific invocation."""

    def validate_input(self, input: dict) -> ValidationResult:
        """Validate parameters before execution."""

Built-in tools to implement first:

Tool	Purpose	Priority
bash	Execute shell commands	Must-have
read_file	Read file contents	Must-have
write_file	Create/overwrite files	Must-have
edit_file	Find-and-replace in files	Must-have
glob	Find files by pattern	Must-have
grep	Search file contents	Must-have
web_search	Search the internet	Nice-to-have
web_fetch	Fetch URL content	Nice-to-have
ask_user	Ask user a question	Nice-to-have

Load references/tool-system.md for complete tool implementations with Zod/Pydantic schemas.

Phase 2.3: The Permission System

Defense-in-depth permission checking:

def check_permission(tool_name, tool_input, context):
    # Layer 1: Always-deny rules (hardcoded safety)
    if matches_deny_rule(tool_name, tool_input):
        return DENY

    # Layer 2: Tool-specific checks
    tool_check = tools[tool_name].check_permissions(tool_input)
    if tool_check in (ALLOW, DENY):
        return tool_check

    # Layer 3: Always-allow rules (user-configured patterns)
    if matches_allow_rule(tool_name, tool_input):
        return ALLOW

    # Layer 4: Safety checks for sensitive paths
    if is_sensitive_path(tool_input):
        return ASK

    # Layer 5: Ask the user
    return ASK

Permission rule format: "ToolName(pattern)" — e.g., "Bash(git *)", "Read", "Edit(*.ts)"

Load references/permission-system.md for the complete 7-layer permission pipeline.

Phase 2.4: Context Management

The #1 challenge in production agents. Implement these 3 strategies:

Strategy 1: Auto-Compact — When approaching context limits, summarize old messages:

if token_count(messages) > context_window - buffer:
    messages = await summarize_old_messages(messages, keep_recent=5)

Strategy 2: Tool Result Budget — Persist large outputs to disk, keep summaries in context:

if len(tool_result.content) > MAX_INLINE_SIZE:
    path = save_to_disk(tool_result.content)
    tool_result.content = f"[Result too large. Full output at: {path}]"

Strategy 3: Micro-Compact — Replace old tool results with [Old tool result cleared]:

for msg in messages[:-RECENT_WINDOW:]:
    if msg.type == "tool_result" and msg.age > TURNOVER_THRESHOLD:
        msg.content = "[Old tool result content cleared]"

Load references/context-management.md for the complete context lifecycle.

Phase 2.5: System Prompt Assembly

Build the system prompt as a string array, not a single blob:

def build_system_prompt(tools, context):
    parts = []

    # Static prefix (cacheable)
    parts.append(identity_section())        # "You are..."
    parts.append(tool_instructions(tools))  # Per-tool usage rules
    parts.append(output_rules())            # Formatting, verbosity

    # Dynamic boundary (changes per turn)
    parts.append(current_datetime())        # Timestamp
    parts.append(git_context(context))      # Branch, status
    parts.append(user_context(context))     # CLAUDE.md or project config

    return parts

Key pattern: Split static/dynamic content with a cache boundary. Static prefix gets cached by the API (10x cheaper). Dynamic suffix changes every turn.

Load references/system-prompts.md for the complete prompt template.

Phase 3: Add the Interaction Layer

Choose your UI:

Interface	Framework	Best For
Terminal TUI	Ink (React) / Rich (Python)	Developer tools, CLIs
Web UI	React + SSE	User-facing products
API Server	FastAPI / Express	Headless agents, integrations
Headless CLI	argparse + streaming	CI/CD, automation

Terminal UI component tree (React/Ink pattern):

App
  Messages          # Virtual scrollable message list
  Spinner            # "Thinking..." / "Running bash..." indicator
  PromptInput        # User input with history, autocomplete
  PermissionDialog   # "Allow Bash(git push)?" prompt
  CostDisplay        # Running token/cost counter

Phase 4: Add Advanced Features

These make a good agent into a great one:

Feature	Description	Reference
Sub-agents	Spawn child agents for parallel work	agent-loop.md
Hooks	Pre/post tool execution callbacks	permission-system.md
Skill system	Load dynamic capabilities at runtime	See templates
Slash commands	/compact, /review, /cost etc.	See templates
Streaming output	Show results as they arrive	agent-loop.md
Cost tracking	Per-turn token and cost reporting	architecture.md
Session persistence	Save/restore conversations	memory.md
Long-term memory	Facts that persist across sessions	memory.md
MCP integration	Plug in external tool servers	mcp.md

Phase 5: Validate and Ship

Run the validation script to verify your agent has all critical components:

python scripts/validate_agent.py ./my-agent

This checks:

• Agent loop handles tool_use and text responses
• Tool registry with proper schemas
• Permission system with deny/allow/ask
• Context compaction for long conversations
• System prompt assembly
• Error handling for API failures
• Cost tracking

Templates

Ready-to-use starting points. Copy and customize:

Template	Files	Description
minimal-agent	3 files	Single-purpose agent with 2 tools
coding-agent	7 files	Full coding assistant with 9 tools
research-agent	5 files	Research and analysis agent
task-agent	9 files	Multi-step orchestration agent
workflow-agent	8 files	Planner + executor + HITL checkpoints
api-agent	6 files	HTTP/SSE server for embedding in apps

Examples

Complete walkthroughs of real agent builds:

Example	What It Builds	Stack
python-coding-agent	Full Claude Code clone	Python + Anthropic SDK + rich
typescript-research-agent	Streaming research assistant	TypeScript + Bun + Anthropic SDK
multi-agent-orchestrator	Agent that spawns sub-agents	Python + asyncio

Scripts

Script	Purpose
scripts/validate_agent.py	Validate agent has all required components
scripts/scaffold.py	Generate agent project from template

Reference Files

Deep-dive documentation for each subsystem. Load when you need implementation details:

File	Contents
references/architecture.md	Complete Claude Code architecture overview
references/agent-loop.md	The streaming agent loop with error recovery (Python)
references/typescript-agent-loop.md	Full TypeScript/Bun async generator agent loop
references/tool-system.md	Tool registry, schemas, and execution pipeline
references/permission-system.md	7-layer permission pipeline with hooks
references/context-management.md	Context window lifecycle and compaction
references/system-prompts.md	Prompt assembly with cache optimization
references/mcp.md	MCP server integration — plug in external tools
references/memory.md	Session persistence and long-term memory system
references/rust-agent.md	Full Rust implementation with tokio + reqwest
references/production-principles.md	85% compounding problem, Manus 6 principles, production checklist
references/planner-executor.md	Three-agent pattern: planner + executor + verifier with dependency graph
references/human-in-the-loop.md	HITL approval matrix, tiered delegation, terminal + webhook notifications
references/serving.md	FastAPI + SSE server, multi-user sessions, React EventSource frontend
references/long-tasks.md	File-based planning for 100+ step tasks, context handoff, resume protocol
references/observability.md	Trace system, OpenTelemetry, LangSmith, cost/latency/error dashboards
references/cost-optimization.md	Prompt caching, model routing, context discipline, batch API
references/framework-guide.md	LangGraph vs CrewAI vs Mastra vs Vercel AI SDK — decision matrix + examples